Computers enable the collection and storage of vast amounts of data in easily accessible databases. Patterns, connections, and other features of this data may provide valuable insights, but the volume of the information may present challenges for analysis. Visualizations may be used to overcome these challenges by representing aspects of the data in a visual manner, e.g., in a graph or diagram. Visualizations of a large data set may be substantially more intuitive and useful than, e.g., a textual representation of the underlying data or a set of statistics drawn from the data.
Like other aspects of large-scale data analysis, producing visualizations may be challenging when the input data is not uniformly structured. Inconsistent structure is particularly common in data drawn from many different sources, which people are increasingly interested in analyzing. Data integration platforms have been created to combine data from different sources for the purpose of analysis, but the visualization functionality they provide may be limited.
A visualization that is useful for drawing insights from one data set may not be useful for drawing insights from another data set. Therefore, some platforms may support a variety of different visualizations. Each type of visualization may have unique limitations. For example, certain visualizations may be useful for analyzing only a few specific kinds of data. Other visualizations may be useful for trained analysts following specific lines of inquiry, but may not be useful for lay persons needing an intuitive overview of relevant information. There is a need for visualizations that overcome these limits, with broad utility and intuitive readability.